The tauopathies are a diverse group of age-related neurodegenerative diseases that are characterized by the development of neurofibrillary tangles and other Tau inclusions in specific regions of the brain. These conditions are progressive and debilitating, and are ultimately fatal. Each tauopathy has distinct clinical and morphologic features, but all have the characteristic of affecting unique neuronal populations. In Alzheimer's Disease (AD), which affects over 5 million Americans, the tauopathy occurs in the setting of amyloid depositions and causes degeneration of limbic and association cortices, sparing adjacent motor and sensory regions. Other tauopathies, including frontotemporal dementia (FTD), are lobar degenerations. The continuing search for effective therapies is crippled by the lack of knowledge pertaining to the molecular mechanisms underlying the anatomical specificity and mechanisms of progression of neurodegenerative disease. Our goal in this study is to identify novel therapeutic targets by pinpointing those signaling pathways that are dysregulated in specific regions of the brain during the onset and progression of neurodegeneration. Our approach to identifying these pathways is to develop quantitative, data-driven computational models of cellular signaling in the brains of mouse models of FTD and AD. Our driving hypothesis, arising from a bioengineering analysis perspective, is that neuro-degeneration is associated with a deviation of the neuronal multi-pathway signaling network 'state' from normal, such that perturbation of this 'state' can modulate the onset and progression of neurodegenerative disease in ways predictable from a computational model characterizing the network-phenotype relationship. Computational modeling is required to provide novel insights, not readily ascertained from intuitive inspection of the associated complex data-sets, into the integrative operation of key pathways that govern how neurons respond to the insults that ultimately result in cell death. Importantly, this bioengineering-based perspective will also help to generate new, multi-variate corollary hypotheses relating to the phenotypic effects of pathway perturbation. In essence, the computational models derived from signaling datasets generated from mouse models of neurodegeneration will identify signaling pathways that can be modulated to control disease onset and progression.